Error metrics for characters

ABSTRACT

Generating an error from an error metric quantifying differences between reference objects representing characters and representations of the reference objects. One embodiment includes a method which includes accessing a reference object representing a character. One or more reference object characteristics are quantified. The reference object characteristics are related to character structural and color information of at least a portion of the reference object to generate a reference object metric. A representation object of the reference object is accessed. One or more representation object characteristics are quantified to create a representation object metric. The representation object characteristics are related to character structural and color information of a portion of the representation object of the reference object corresponding to the portion of the reference object. An error is calculated based on a difference between the reference object metric and the representation object metric. The error is output to a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior application Ser. No.11/768,835 filed Jun. 26, 2007, entitled “ERROR METRICS FOR CHARACTERS”,which is incorporated herein by reference in its entirety.

BACKGROUND

Computers and computing systems have affected nearly every aspect ofmodern living. Computers are generally involved in work, recreation,healthcare, transportation, entertainment, household management, etc.

Computers generally include hardware which allows the computer to outputvisual representations of data to a user. For example, computers mayinclude a video card connected to a CRT monitor or LCD video screenwhich allows visual representations of objects including characters orother graphics to be displayed to the user. Similarly, many computersinclude the ability to interface with printers to provide visualrepresentations of objects on a hard copy for a user.

Depending on the nature of the visual representations and the mediumused to convey the visual representations to the users, the visualrepresentations output to a user may vary from an ideal output. Forexample, LCD video screens are typically only able to output data usinga pixel resolution of about 110-120 pixels per inch. Outputting a visualrepresentation of a character may result in the character includingaliasing due to the size and resolution limitations of a screen.

To correct less than ideal representations, filters may be applied to anobject. For character objects, filters are applied to the entire object.Thus, in the example above, an antialiasing filter such as gray scalingmay be applied to an entire character to help soften the effects of thealiasing.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one exemplary technology area where some embodimentsdescribed herein may be practiced.

BRIEF SUMMARY

One embodiment described herein is directed to a method including actsfor generating an error from an error metric quantifying differencesbetween reference objects representing characters and representations ofthe reference objects. The method includes accessing a reference objectrepresenting a character. One or more reference object characteristicsare quantified. The reference object characteristics are related tocharacter structural and color information of at least a portion of thereference object to generate a reference object metric. A representationobject of the reference object is accessed. One or more representationobject characteristics are quantified to create a representation objectmetric. The representation object characteristics are related tocharacter structural and color information of a portion of therepresentation object of the reference object corresponding to theportion of the reference object. An error is calculated based on adifference between the reference object metric and the representationobject metric. The error is output to a user.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of the teachings herein. Features andadvantages of the invention may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. Features of the present invention will become more fullyapparent from the following description and appended claims, or may belearned by the practice of the invention as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof the subject matter briefly described above will be rendered byreference to specific embodiments which are illustrated in the appendeddrawings. Understanding that these drawings depict only typicalembodiments and are not therefore to be considered to be limiting inscope, embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates a reference object, a representation of the referenceobject, and a second representation of the reference object;

FIG. 2A illustrates a representation of a reference object using grayscaling;

FIG. 2B illustrates a high resolution bitmap of a character;

FIG. 2C illustrates a lower resolution bitmap of the character in FIG.2B; and

FIG. 3 illustrates a method of calculating an error.

DETAILED DESCRIPTION

Embodiments herein may comprise a special purpose or general-purposecomputer including various computer hardware, as discussed in greaterdetail below.

Often an object such as a character that is output to a user varies froman idealized character. For example, a character that is displayed to ascreen which only has a whole pixel resolution of 100-120 dots per inchmay vary in various ways from an idealized representation of thecharacter, such as a high resolution version of the character or adrawing of the character. Embodiments describe herein includefunctionality for quantifying the differences between a reference objectof a character and a representation of the reference object. This may beaccomplished by quantifying various characteristics of various featuresof the reference object and quantifying various characteristics ofvarious features of the representation of the reference object andcalculating a difference based on the quantities. Characters mayinclude, for example, writing symbols in various languages, iconsymbols, ASCII symbols, line drawing elements, etc.

Referring now to FIG. 1, an example of one embodiment is demonstrated.FIG. 1 illustrates a reference object 100. In this example, thereference object 100 is a high resolution character object. Notably, thereference object may be any suitable object such as a high resolutionrendition of the character, an outline representation of a character, adrawing on paper, or any other suitable reference object including alower quality object.

FIG. 1 further illustrates two representations 102 and 104 of thereference object 100. The representation 102 in this example hasnoticeable aliasing. Aliasing typically occurs because a visual outputis limited to discrete units of display. For example, a computer LCDscreen is typically limited to 100-120 whole pixels per inch. Thus, whencharacters are rendered using whole pixels, the number of pixelsavailable with respect to the size of the character may result in wholepixel boundaries causing the jagged edges characteristic of aliasing.

In one embodiment, the representation 102 may be compared to thereference object 100. This may be accomplished by using an error metric,where one or more characteristics of the reference object 100 arecompared to one or more characteristics of the representation 102. Anerror, or difference between the two, is calculated based on thecomparison. The characteristics of the reference object and therepresentation of the reference object may be quantified to a referenceobject metric and a representation object metric respectively which maybe used in the calculation of the error.

Notably, when calculating an error using an error metric, the errormetric will typically take into account both a characteristic beingmeasured, and spaces in which the characteristic is measured.Characteristics may be thought of informally as “what” and may include“where” measurements are being made. Spaces define “how” to quantify themeasurement

Quantities may be calculated for several characteristics as will bediscussed in more detail below. Examples of characteristics include, forpurposes of example here, luminosity, and contrast. Thesecharacteristics could be measured with respect to a variety of differentfeatures, including typographical & geometrical entities andcharacteristics. One example of a mathematical definition of contrastincludes a 1^(st) order derivative of a characteristic for which thecontrast is measured.

A variety of spaces exist in which an error can be calculated. Forexample, in one embodiment, a metric can be measured based on suchspaces as CIE-defined standard spaces (CIELAB, S-CIELAB) or theircomponents, space of spectral power distributions, space of human coneresponses or in some consequent spaces for example such as space of1^(st) (or any) order derivatives.

Comparisons may be performed based on the common characteristics ofcharacters as compared to general objects. For example, characters,including text bodies, tend to have higher spatial frequencies thangeneral objects. Additionally, characters typically have very highcontrast. For example, characters used in text settings are generallyblack and white. Because of this, characters typically should berendered such that perceived contrast of different components of acharacter closely resembles those of the reference object. As such, acomparison of the reference object's perceived contrast or perceivedcolorfulness can be compared to the perceived contrast or perceivedcolorfulness of the representation 102.

Characters and fonts themselves have other common characteristics thatare generally less relevant to general objects. For example, it isgenerally desirable that characters have open counter spaces, such asthe counter space 106 in FIG. 1. Additionally, it is generally desirablethat strokes should be consistent. For example, strokes should berendered without visual interruptions. Additionally, strokes should haveconsistent widths on stems or other features. Further still, charactersshould have regularity across the character. For example, the letter “m”should be rendered such that each of the three stems is consistent withone another. Further, it is desirable that characters be rendered suchthat strokes do not collide with one another.

Thus, comparisons between the reference object 100 and therepresentation 102 may be made based on the expected commoncharacteristics when the object is a character. A difference between areference object 100 and a representation 102 can be calculated based onthe characteristics to calculate an error. This error can then be usedto define the difference between two objects. This may allow foroptimizations to be applied to an object to improve the object or fordetermining if the representation 102 is within a specified parameterrange.

Notably comparisons may be made based on a space including generalcharacteristics of human or other organic species visual perception.Specifically, the physiology of, for example, human visual perceptioncauses some types of differences between the reference object 100 andthe representation 102 to be more perceptible and contribute to moredissatisfaction with the rendering of an object. For example, differentcolor of different hues may contribute differently to perceivedcolorfulness which usually is especially distracting.

Notably, some comparisons may be related to a space of human or otherspecies visual perception for a specific group of individuals. Forexample, individuals with dichromatic vision may have differentperception requirements from those who do not suffer from dichromaticvision. As such, some error metrics may be used where a specific user orclass of users, and their physiological perception characteristics aretaken into account in quantifying an error in an error metriccalculation.

Several different error metrics may be applied singly or in combinationwith other error metrics. Further, error metrics may be weighted suchthat some characteristic errors are weighted as being more importantthan other errors. Error metrics also can be “conditionally weighted.”That is, error metrics may have their relative importance prioritizedbased on some local characteristic of an object. For example, thepresence or absence of a structural feature of a character may affectthe weight of an error metric. Error metrics are implemented to achievea reasonable evaluation of the difference, or a characteristic of adifference, between the rendition of the representation 102 of thereference object 100 and the reference object 100.

Illustrating now additional examples, as noted previously, one errormetric relates to perceived contrast. For example, it is often desirablethat text be perceived as a high contrast black and white object. Thus,the reference object 100, or portions of the reference object 100, maybe evaluated for perceived contrast. The perceived contrast of thereference object 100 may be assigned some reference object metric.Similarly, the representation 102 of the reference object may beevaluated for perceived contrast. The perceived colorfulness of therepresentation 102 of the reference object is also assigned arepresentation object metric. The error is then calculated by evaluatingat least the difference between the reference object metric for theperceived contrast of the reference object 100 and the representationobject metric for the perceived contrast of the representation 102.Metrics for reference objects and corresponding representations forperceived contrast might include, in one example, information about allintensities of all primaries for all pixels. Notably, errors are notnecessarily calculated by just subtracting two numbers, which quantify areference object and its representation respectively, but rather byconsidering an entire set of data represented by the metrics for thereference object and its corresponding representation. In addition,perceived contrast of the different features of objects may beevaluated. For example, perceived contrast of a specific typographicalcomponent, such as stroke, with respect to a background or to anothercomponent may be evaluated. In one embodiment, a metric for contrast maybe mathematically approximated using a 1^(st) order derivative in anappropriate space.

Therefore an error metric calculating deviation between perceivedcontrast of different components, with respect to background or to othercomponents, in a reference object and its representation can be defined.For measuring, comparing and/or optimizing definite characteristics ofan object, certain information is associated with the object. Forexample, an object typically includes information regarding theimmediate surroundings of the object. Further, additional informationcommonly used in science of visual perception, such as informationrelated to a wider surrounding, viewing conditions, and characteristicsof an output device, is generally required and assumed to be known as afunction of being associated with an object. This information mayinfluence a measured characteristic and therefore an outcome of an errormeasurement. Therefore an error metric can be derived or calculated thatis optimized for higher contrast or increased spatial intensities.

Error metrics may be implemented for typographic characteristics. Inparticular, error metrics may be applied based on structural informationincluding typographical entities such as strokes, serifs, counterspaces, etc; geometrical entities and characteristics such asboundaries, connected components, skeletons, orientations, etc; andrelations between entities such as distance and relative orientation.Further, error metrics may be based on color information, includinginformation related to foreground and background. Some typographiccharacteristics which may be evaluated include openness ofcounter-spaces, stroke collisions, stroke continuity, stroke regularity,stroke consistency in rendering, and the like. Illustrating now an errorcalculated based on the openness of the counter space 108 and theopenness of the counter space 106, FIG. 2A illustrates a representation202 where gray scaling has resulted in the counter space 206 appearingmore closed. In one embodiment, a measure of the counter space, such asfor example by calculating cumulative luminosity over an idealizedregion of the counter-space or measuring extends of a counter space byindicating a region with luminosity higher than a threshold value, maybe used along with a measure of a reference object counter spacemeasuring cumulative luminosity over an idealized region of thecounter-space or measuring extends of a counter space by indicating aregion with luminosity higher than a threshold value of the referenceobject counter space to calculate an error of an error metric.

Another error metric that may be used relates to perception of collisionof strokes. Strokes may appear to collide when represented as parallelor nearly parallel. For example, FIGS. 2B and 2C illustrate the effectsof stroke collisions. FIG. 2B illustrates a high resolution bitmap of acharacter 208. FIG. 2C illustrates a representation 210 of the character208, where the representation is a lower resolution bitmap of thecharacter 208. As illustrated at 212, stroke collisions result becauseof the limitations due to the low resolution at which the representation210 is presented. A metric may be implemented which defines theperception of collision of strokes of a reference object as compared tothe perception of collision of strokes of a representation of thereference object. Similarly, error metrics may be used to characterizethe regularity of a stroke, or consistency of a stroke rendering. Forexample, the consistency of strokes in a reference object may becompared with the consistency of strokes in a representation of thereference object. Illustratively, it is desirable that for the letter“m” each of the three stems should be similar in width. Thus theconsistency of widths of stems in the reference object may be comparedwith the consistency of width of stems in the representation.

Notably, a number the error metrics described herein may be specificallydirected to the space of physiological characteristics of human or otherspecies perception including, for example, characteristics of visualperception, characteristics of cognitive processes, or characteristicsof recognition specific for a class of objects being evaluated. Thus,when an error metric is used, the error metric may include elementswhich take into account the physiological characteristics of a singlehuman or other species user, a group of human or other species users, orhuman or other species perception in general. For example, a singlehuman user may use various wizards or other interfaces to allow a systemto determine the user's unique physiological characteristics. Thesecharacteristics could be quantified and used as operands in an errormetric calculation. Similarly, known characteristics for a given groupof users may be included as operands. For example, users withdichromatic vision, also known as colorblindness, have certainperception characteristics which can be described mathematically. Thesemathematical descriptions may be included as part of the error metric.Further, certain perception characteristics are common to major portionsof the population. These perception characteristics can be describedmathematically and included in error metric usage.

Another error metric that may be used relates to local similarities. Forexample, a point by point comparison of a local portion of therepresentation could be made to a corresponding local portion of areference object. Notably, in some embodiments, when a local similarityerror metric is used with other error metrics, the local similarityerror metric may be given a lower error weight when compared to othererror metrics when the other error metrics are deemed to have higherimportance. For example, a geometric distortion, such as a slight shiftin a feature, may be beneficial to achieve higher contrast, or someother characteristic. Thus, if higher contrast is deemed to have moreimportance than local similarity, then the local similarity error metricmay have low or no penalty weight.

When defining many error metrics, there may be a specification wherespatially the characteristics are measured. One approach is by apoint-by-point comparison meaning that the comparison is performedbetween some characteristics measured at exactly the same locations ofthe reference object and its representation.

However while defining error metrics specific for characters, it mightbe beneficial to use a more ‘relaxed’ approach allowing for somecharacteristic of a representation to become similar to thecharacteristic of a reference object within some tolerance of a locationwhere it was measured for the reference object without being penalized.One embodiment of such approach will establish correspondence betweenlocations where a characteristic is measured based on a rule dependingon structural or other information. Another embodiment may comparevariation of some characteristic along a path between pairs ofestablished locations rather than values of a characteristic itself.This “more relaxed” approach may allow, for example, allowing a tradeoffbetween achieving a higher contrast of some structural elements, e.g.strokes and the like, and a spatial distortion with respect to thereference object.

Errors using error metrics may be calculated in a number of differentways. For example, in one embodiment, an error may be calculated byidentifying one or more spaces for comparison where the objects ormappings in the spaces of representations of reference objects andcorresponding reference objects are compared. One or morecharacteristics may be compared at each space. Characteristics at eachspace may be quantified into a metric, where the metrics are used incalculating the error. Notably, when multiple spaces and/orcharacteristics are quantified, an overall error metric may includeweighting of different characteristics or characteristics at differentspaces. Weighting allows for certain characteristics, or characteristicsat certain spaces to be considered more important than others.

In another embodiment, an error may be calculated based on importantentities and the related characteristics of the entities of an objectbeing rendered. For example, white space, strokes, skeletons, edges,orientations, distances etc. may be identified on a reference object anda representation of the reference object. Comparisons can then be madeof the characteristics for these identified entities. Additionally, whenmultiple entities or characteristics are evaluated, relative weights maybe assigned to characteristics of the entities based on relativeimportance of the entities or characteristics of the entities.

Referring now to FIG. 3, an example of a method 300 is illustrated. Themethod 300 illustrates various acts for calculating an error using anerror metric. The method 300 may be practiced, for example, in acomputing system. The method 300 includes acts for generating an errorquantifying differences between reference objects representingcharacters and representations of the reference objects. The method 300includes accessing a reference object representing a character (act302). As noted, the representation may be a high resolution rendition ofthe character, and outline representation of a character, a drawing onpaper, or any other suitable reference object including a lower qualityobject.

The method 300 further illustrates quantifying one or more referenceobject characteristics related to character structural and colorinformation of the reference object to generate a reference objectmetric (act 304). Notably, this may include quantifying characteristicsof a portion of a character. For example, openness of a counter spacemay be quantified. Characteristics of character structure featuresincluding such as strokes, stems, crossbars, diagonals and serifs may bequantified. For example stroke characteristics may be quantified,including consistency of strokes within a character or acrosscharacters, continuity of strokes, and collisions of strokes. Othercharacteristics that may be quantified include perceived luminance,chrominance and their derivatives such as perceived brightness,colorfulness or contrast, sharpness of edges, or perceived shift ofcharacter features. Still other characteristics may be quantified within the scope of embodiments described herein.

Quantifying may include a number of different actions. For example,quantifying may include calculating pixel primaries, luminosity, CIELABspaces, contrast, etc. Additionally, quantifying may include factorssuch as human or other species perception models.

Referring once again to FIG. 3, method 300 includes accessing arepresentation object of the reference object (act 306). The method 300further includes quantifying one or more representation objectcharacteristics related to character structural and color information ofthe representation object of the reference object to create arepresentation object metric (act 308). This may include quantifyingcharacteristics of a portion of the representation object correspondingto a portion of the reference object. The method 300 further includescalculating an error based on a difference between the reference objectmetric and the representation object metric (act 310).

Method 300 further includes outputting the error to a user (act 312).Outputting the error to a user may be accomplished in a number ofdifferent ways. For example, an error may be displayed to a human user.Alternatively, a user may be another computer system or program within acomputing system. Thus, the error may be output in a digital formatconsumable by digital systems. This allows for the error to be used inlogging operations, automated error correction operations, etc.

Embodiments may also include computer-readable media for carrying orhaving computer-executable instructions or data structures storedthereon. Such computer-readable media can be any available media thatcan be accessed by a general purpose or special purpose computer. By wayof example, and not limitation, such computer-readable media cancomprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to carry or store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.When information is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

1. A method of generating and outputting an error quantifyingdifferences between reference objects representing characters andrepresentations of the reference objects, wherein the method includes:accessing a reference object representing a character; quantifying oneor more reference object characteristics, the reference objectcharacteristics being related to character structural or colorinformation of at least a portion of the reference object to generate areference object metric; accessing a representation object of thereference object; quantifying one or more representation objectcharacteristics, the representation object characteristics being relatedto character structural or color information of a portion of therepresentation object of the reference object corresponding to theportion of the reference object to create a representation objectmetric; determining and quantifying physiological characteristics of auser; calculating an error based on a difference between the referenceobject metric and the representation object metric and which iscalculated by at least considering the physiological characteristics ofthe user as operands within the error calculation; and outputting thecalculated error.
 2. The method of claim 1, wherein the error metric iscalculated using mathematical descriptions that define perceptioncharacteristics associated with the physiological characteristics of theuser.
 3. The method of claim 2, wherein the physiologicalcharacteristics of the user comprise dichromatic vision and wherein theerror metric is calculated using mathematical descriptions that defineperception characteristics associated with colorblindness.
 4. The methodof claim 1, wherein the physiological characteristics of the user areobtained through a computing interface that determines the physiologicalcharacteristics of the user.
 5. The method of claim 1, wherein theportion of the reference object includes character structuralinformation including at least one of: typographical entities includingone or more of strokes, serifs, and counter spaces; geometrical entitiesand characteristics including one or more of boundaries, connectedcomponents, skeletons, and orientations; or relations between entitiesincluding one or more of distance and relative orientation.
 6. Themethod of claim 1, wherein the portion of the reference object includesa character counter space.
 7. The method of claim 1, wherein the one ormore reference object characteristics includes openness of the counterspace.
 8. The method of claim 1, wherein the reference object metric iscalculated using a first or higher order derivative or approximation ofa first or higher order derivative of a portion in an appropriate spaceof the reference object to determine contrast of the reference object inthe portion.
 9. The method of claim 1, wherein the reference objectmetric is calculated using a human or other organic species perceptionmodel, including at least one of characteristics of visual perception,characteristics of cognitive processes, or characteristics ofrecognition specific for a class of objects being evaluated.
 10. Themethod of claim 9, wherein the reference object metric is calculatedusing at least one of local luminance or chrominance and theirderivatives, including at least one of perceived contrast or perceivedcolorfulness, sharpness of edges, or perceived shift of characterfeatures based on different spaces.
 11. The method of claim 1, whereincalculating an error is performed based representations of objects' edgepixels in relation to a background color.
 12. The method of claim 1,wherein calculating an error based on a difference between the referenceobject metric and the representation object metric comprises allowingfor at least one characteristic of the representation object to besimilar to the characteristic of the reference object within apre-specified tolerance of a location where the characteristic wasmeasured for the reference object without being penalized.
 13. Themethod of claim 1, wherein calculating an error based on a differencebetween the reference object metric and the representation object metriccomprises comparing characteristics at locations established by a rulebased on a structural information including information related totypographical features and entities.
 14. A computer readable storagemedium comprising stored computer executable instructions that whenexecuted by a processor are configured to perform a method of generatingan error from an error metric quantifying differences between referenceobjects representing characters and representations of the referenceobjects, the method comprising: accessing a reference objectrepresenting a character; quantifying one or more reference objectcharacteristics, the reference object characteristics being related tocharacter structural or color information of at least a portion of thereference object to generate a reference object metric; accessing arepresentation object of the reference object; quantifying one or morerepresentation object characteristics, the representation objectcharacteristics being related to character structural or colorinformation of a portion of the representation object of the referenceobject corresponding to the portion of the reference object to create arepresentation object metric; determining and quantifying physiologicalcharacteristics of a user; calculating an error based on a differencebetween the reference object metric and the representation object metricand which is calculated by at least considering the physiologicalcharacteristics of the user as operands within the error calculation;and outputting the calculated error.
 15. The computer readable storagemedium of claim 14, wherein the computer readable storage mediumcomprises a computing system having system memory storing the computerexecutable instructions.
 16. A method of generating an error quantifyingdifferences between reference objects representing characters andrepresentations of the reference objects, wherein the method includes:accessing a reference object representing a character; quantifying aplurality of different object characteristics, the reference objectcharacteristics being related to different character structural or colorinformation of the reference object; assigning weights to the quantifiedobject characteristics and generating a corresponding reference objectmetric from the weighted and quantified object characteristics;accessing a representation object of the reference object; quantifying aplurality of representation object characteristics, the representationobject characteristics being related to character structural or colorinformation of the representation object of the reference object;assigning weights to the quantified object characteristics andgenerating a corresponding reference object metric from the weighted andquantified object characteristics; calculating an error based on adifference between the reference object metric and the representationobject metric; and outputting the calculated error.
 17. The method ofclaim 16, wherein the method further includes: determining andquantifying physiological characteristics of a user; and calculating theerror by at least considering the physiological characteristics of theuser as operands within the error calculation.
 18. The method of claim17, wherein the error metric is calculated using mathematicaldescriptions that define perception characteristics associated with thephysiological characteristics of the user.
 19. The method of claim 18,wherein the physiological characteristics of the user comprisedichromatic vision and wherein the error metric is calculated usingmathematical descriptions that define perception characteristicsassociated with colorblindness.
 20. The method of claim 19, wherein thephysiological characteristics of the user are obtained through acomputing interface that determines the physiological characteristics ofthe user.